@Dan_Jeffries1: 终于有一篇关于Mythos的半有用读物,不涉及神话,而是更实际地谈论这意味着什么(不是……

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摘要

Dan Jeffries评论了Cloudflare对Anthropic的Mythos的测试,认为真正的讨论应聚焦于针对AI驱动的攻击的实际安全改进,并且如果团队调整工作流程,AI最终会让软件更安全。

Finally a semi-useful read on Mythos that is free of myth and talks about what this means more practically (not this is the end of the world as we know it, but how do we deal with faster patches and attacks from AI as other models scale to chained exploits)? This is the kind of conversation we need, not idiotic ones about the end of all software. We need "what is the right answer?" because these models are coming and will get better so how to we put our heads together and make better/more secure software across the world? And it can't just be patching the 100 or so projects that got access to Project Glasswing. That is not gonna help the world. We need to figure out how does everyone else who is not part of the special chosen people to get blessed with access to test and patch their stuff, aka the open source projects and closed software that is not Office or Cloudflare but the 99.99% of software that runs everything else in the world? What is the right loop cycle to help people patch and fix things at the source? In the long run, AI will make software more secure, not less. But it will change how teams have to work to get there. Figuring that out means putting it in more team's hands sooner rather than later.
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终于有一篇关于Mythos的半实用文章,没有神话,更实际地讨论了这意味着什么(不是我们熟知的世界末日,而是当其他模型扩展到链式利用时,我们如何应对更快的补丁和来自AI的攻击)?

这才是我们需要的对话,而不是关于所有软件终结的愚蠢言论。

我们需要“正确答案是什么?”因为这些模型即将到来,并且会变得更好,所以我们如何集思广益,在全球范围内制造更好/更安全的软件?

而且不能只修补那大约100个获得了Project Glasswing访问权限的项目。

这无助于世界。

我们需要弄清楚,那些不属于特殊选中的、有幸获得测试和修补权限的其他人,即开源项目和闭源软件(不是Office或Cloudflare,而是运行世界上其他一切的99.99%的软件)该怎么办?

什么是正确的循环周期,帮助人们在源头修补和修复问题?

从长远来看,AI会使软件更安全,而不是更不安全。

但这将改变团队走向这一目标的方式。

弄清楚这一点意味着要尽早将其交到更多团队手中,而不是拖延。

Cloudflare (@Cloudflare): Cloudflare的安全团队在过去几周内,针对我们自己的五十个代码库测试了Anthropic的Mythos。我们学到了关于进攻性AI的知识,为什么更快的补丁是错误的反应,以及围绕漏洞的架构下一步应该是什么样子。

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Project Glasswing: Mythos 的启示

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Cloudflare 测试了 Anthropic 专为安全漏洞研究设计的 Mythos Preview 大语言模型,发现它能够将多个漏洞串联成利用链并生成可行的验证代码,这代表了相较于通用前沿模型的重大进步。